ارائه یک مدل ترکیبی برای شناسایی و تحلیل الگوهای معنی‌دار در نمودارهای کنترل فرآیند

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشگاه آزاد اسلامی واحد قم

2 دانشگاه آزاد اسلامی واحد نجف‌آباد

3 دانشگاه صنعتی امیرکبیر

چکیده

شناسایی صحیح و طبقه‌بندی دقیق الگوهای معنی‌دار در نمودارهای کنترل فرآیند آماری از نظر آنکه رفتارهای غیرطبیعی را تداعی می‌کنند بسیار بااهمیت است. تشخیص و استخراج الگوهای غیرطبیعی، حساسیت نمودارهای کنترلی را در شناسایی وضعیت‌های خارج از کنترل افزایش می‌دهد. در سال‌های اخیر به دلیل توانمندی‌های‌ شبکه‌های عصبی مصنوعی، از آن‌ها برای شناسایی الگوهای غیرطبیعی در نمودارهای کنترلی شوهارت استفاده شده است. اغلب این پژوهش‌ها، بویژه هنگامی‌که حساسیت فرآیند نسبت به رخداد الگوهای غیرطبیعی بالا باشد، دچار خطای طبقه‌بندی نادرست الگوها می‌شوند. در این پژوهش، مدل ترکیبی مبتنی بر شبکه‌های LVQ و MLP و همچنین خط برازش نمونه‌ها برای شناسایی و تجزیه‌وتحلیل الگوهای غیرطبیعی پایه در نمودارهای کنترل فرآیند ارائه شده است. این مدل پیشنهادی، علاوه بر اینکه در سطوح مختلف حساسیت، خطای طبقه‌بندی نادرست الگوها را به مقدار زیادی کاهش می‌دهد، رخداد همزمان الگوهای پایه را شناسایی و پارامترهای متناظر را برآورد می‌کند. در نهایت با بکارگیری نمونه‌های شبیه‌سازی‌شده، کارآمدی و اثربخشی مدل نشان داده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Development of a Hybrid Model for Recognition and Analysis of Significant Patterns in Process Control Charts

نویسندگان [English]

  • Ahmad Koochakzadeh 1
  • Seyyed Ali Lessany 2
  • Seyyed Mohammd Taghi Fatemi Ghomi 3
1 Qom Branch, Islamic Azad University
2 Njafabad Branch, Islamic Azad University
3 Amirkabir University of Technology
چکیده [English]

Correct recognition and precise classification of significant patterns in statistical process control charts is unavoidable. Because these unnatural patterns associate out of control conditions. In fact, extraction of unnatural patterns increases the sensitivity of control charts in identification of out of control states. In recent years, because of the abilities of artificial neural networks in patterns recognition, these networks have been used to discriminate unnatural patterns in Shewart control charts. In most of such studies, the misclassification error of patterns is remarkable, especially when the desired sensitivity of process is at high value. This paper proposes a hybrid model for the recognition and analysis of the basic patterns in process control charts using LVQ and MLP networks along with examining the fitted line of sample points. In the proposed model not only the misclassification error at different levels of sensitivity decreases considerably, but when basic patterns occur concurrently, the possibility of recognition of patterns and assessment of their corresponding parameters will be provided too. The efficiency and effectiveness of the model have been tested by simulated samples.

کلیدواژه‌ها [English]

  • Significant patterns
  • Statistical process control
  • Fitted line of samples
  • LVQ network
  • MLP network

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